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Measuring Dengue Virus RNA in the Culture Supernatant of Infected Cells by Real-time Quantitative Polymerase Chain Reaction
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Dengue outbreaks: unpredictable incidence time series.

A F B Gabriel1, A P Alencar2, S G E K Miraglia1

  • 1Instituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF), Laboratório de Economia, Saúde e Poluição Ambiental, Universidade Federal de São Paulo - UNIFESP,São Paulo,Brazil.

Epidemiology and Infection
|March 15, 2019
PubMed
Summary

This study analyzed dengue fever temporal patterns in Ribeirão Preto and São Paulo, Brazil. SARIMA models forecast cases but have wide confidence intervals, requiring cautious interpretation for public health planning.

Keywords:
ForecastSARIMAdengue

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Area of Science:

  • Epidemiology
  • Public Health
  • Time Series Analysis

Background:

  • Dengue fever incidence is rising globally, affecting new regions.
  • Ribeirão Preto and São Paulo, Brazil, show high dengue incidence, particularly post-2009 and 2013.
  • Accurate dengue forecasting is crucial for public health preparedness.

Purpose of the Study:

  • To analyze the temporal behavior of dengue cases in Ribeirão Preto and São Paulo.
  • To forecast dengue incidence using time series models, specifically SARIMA.
  • To highlight the importance of wide confidence intervals in forecasting.

Main Methods:

  • Time series analysis was employed.
  • Seasonal Autoregressive Integrated Moving Average (SARIMA) models were fitted to dengue incidence data.
  • Out-of-sample forecasting was performed.

Main Results:

  • SARIMA models provided a satisfactory fit for dengue incidence data in both municipalities.
  • Forecasts generated wide confidence intervals, indicating significant uncertainty.
  • The potential underestimation of epidemic magnitude due to forecast variability was noted.

Conclusions:

  • SARIMA models can aid in anticipating dengue disease scenarios.
  • Forecasts must be interpreted with caution due to wide confidence intervals.
  • Public health services should consider model limitations when planning interventions for dengue outbreaks.